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Information-Enriched Selection of Stationary and Non-Stationary Autoregressions using the Adaptive Lasso

Thilo Reinschlüssel, Martin C. Arnold

arXiv 26 Feb 2024 · Statistics — Methodology

arXiv:2402.16580 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We propose a novel approach to elicit the weight of a potentially non-stationary regressor in the consistent and oracle-efficient estimation of autoregressive models using the adaptive Lasso. The enhanced weight builds on a statistic that exploits distinct orders in probability of the OLS estimator in time series regressions when the degree of integration differs. We provide theoretical results on the benefit of our approach for detecting stationarity when a tuning criterion selects the $\ell_1$ penalty parameter. Monte Carlo evidence shows that our proposal is superior to using OLS-based weights, as suggested by Kock [Econom. Theory, 32, 2016, 243-259]. We apply the modified estimator to model selection for German inflation rates after the introduction of the Euro. The results indicate that energy commodity price inflation and headline inflation are best described by stationary autoregressions.

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Zou, Hui (2006) The Adaptive Lasso and Its Oracle Properties0.92843100%
2Kock, Anders Bredahl (2016) Consistent and conservative model selection with the adaptive lasso in stationary and nonstationary autoregressions0.90924575%
3Efron, Bradley, Hastie, Trevor, Johnstone, Iain, Tibshirani, Robert (2004) Least Angle Regression0.7948275%
4Herwartz, Helmut, Siedenburg, Florian (2010) A New Approach to Unit Root Testing0.73732100%
5Liao, Zhipeng, Phillips, Peter C. B (2015) Automated estimation of vector error correction models0.73732100%
6Silverman, Bernard W (1986) Density estimation for statistics and data analysis0.73732100%
7Caner, Mehmet, Knight, Keith (2013) An alternative to unit root tests: Bridge estimators differentiate between nonstationary versus stationary models and select opt…0.64422100%
8Perron, Pierre, Ng, Serena (1998) An autoregressive spectral density estimator at frequency zero for nonstationarity tests0.64422100%
9Schwert, G. William (1989) Tests for Unit Roots: A Monte Carlo Investigation0.64422100%
10Tibshirani, Ryan J., Taylor, Jonathan (2011) The solution path of the generalized lasso0.64422100%

Showing the top 10 of 39 scored citations.